Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network

doi:10.19799/j.cnki.2095-4239.2021.0503

Abstract

Abstract:

To improve the speed and accuracy of estimating the state-of-health (SOH) of decommissioned batteries, for retired prismatic lithium-iron-phosphate batteries of certain electric buses, eight batteries were selected to continue the cyclic aging experiment, and electrochemical impedance tests were conducted for different cycles. According to the impedance characteristics of lithium-ion batteries, the real part, imaginary part, and modulus at 300 Hz, 60 Hz, and 1 Hz were extracted as characteristic parameters, which reduced the test time from 10 min to a few seconds. Using characteristic parameters as input parameters, combined with BP neural network algorithm, we developed a fast estimation model of retired battery health based on electrochemical impedance and BP neural network and verified the model using 19 datasets that did not participate in model training. The average absolute percentage error of the verified sample's SOH estimate was 1.46%, and the root-mean-square error was 1.60%. The results show that the overall error is low. The method has high estimation accuracy and short test time, realizes rapid estimation of the health status of retired batteries, and is conducive for practical applications.

Key words: electrochemical impedance spectroscopy, BP neural network, retired battery, state-of-health

CLC Number:

TM 912

Mengmeng GENG, Maosong FAN, Kai YANG, Guangjin ZHAO, Zhen TAN, Fei GAO, Mingjie ZHANG. Fast estimation method for state-of-health of retired batteries based on electrochemical impedance spectroscopy and neural network[J]. Energy Storage Science and Technology, 2022, 11(2): 673-678.

Figures/Tables 7

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References 15

1	范茂松, 金翼, 杨凯, 等. 退役LiFePO₄电池性能测评及储能应用[J]. 储能科学与技术, 2019, 8(2): 408-414.
	FAN M S, JIN Y, YANG K, et al. Testing of the performance and energy-storage applied for retired LiFePO₄ batteries[J]. Energy Storage Science and Technology, 2019, 8(2): 408-414.
2	李肖辉, 陈北海, 古领先, 等. 锂离子电池健康状态评估方法研究进展[J]. 电源技术, 2021, 45(6): 818-822.
	LI X H, CHEN B H, GU L X, et al. Research progress of health assessment methods for lithium ion batteries[J]. Chinese Journal of Power Sources, 2021, 45(6): 818-822.
3	吴盛军, 袁晓冬, 徐青山, 等. 锂电池健康状态评估综述[J]. 电源技术, 2017, 41(12): 1788-1791.WU S J, YUAN X D, XU Q S, et al. Review on lithium-ion battery health state assessment[J]. Chinese Journal of Power Sources, 2017, 41(12): 1788-1791
4	CUI Y Z, ZUO P J, DU C Y, et al. State of health diagnosis model for lithium ion batteries based on real-time impedance and open circuit voltage parameters identification method[J]. Energy, 2018, 144: 647-656.
5	刘鹏, 梁新成, 黄国钧. 锂离子电池模型综述[J]. 电池工业, 2021, 25(2): 106-112.
	LIU P, LIANG X C, HUANG G J. A review of lithium-ion battery models[J]. Chinese Battery Industry, 2021, 25(2): 106-112.
6	YANG D, WANG Y J, PAN R, et al. State-of-health estimation for the lithium-ion battery based on support vector regression[J]. Applied Energy, 2018, 227: 273-283.
7	吴铁洲, 刘思哲, 张晓星, 等. 基于FA-BP神经网络的锂离子电池SOH估算[J]. 电池, 2021, 51(1): 21-25.
	WU T Z, LIU S Z, ZHANG X X, et al. SOH estimation of Li-ion battery based on FA-BP neural network[J]. Battery Bimonthly, 2021, 51(1): 21-25.
8	KAUR K, GARG A, CUI X J, et al. Deep learning networks for capacity estimation for monitoring SOH of Li-ion batteries for electric vehicles[J]. International Journal of Energy Research, 2021, 45: 3113-3128.
9	WANG S L, FERNANDEZ C, YU C M, et al. A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm[J]. Journal of Power Sources, 2020, 471: 228450.
10	卢婷, 杨文强. 锂离子电池全生命周期内评估参数及评估方法综述[J]. 储能科学与技术, 2020, 9(3): 657-669.LU T, YANG W Q. Review of evaluation parameters and methods of lithium batteries throughout its life cycle[J]. Energy Storage Science and Technology, 2020, 9(3): 657-669
11	SAHAR K, DANIAL K, HAMIDREZA B, et al. Online health diagnosis of lithium-ion batteries based on nonlinear autoregressive neural network[J]. Applied Energy, 2021, 282: 116159.
12	ZHANG Q, WANG D F, YANG B W, et al. Electrochemical model of lithium-ion battery for wide frequency range applications[J]. Electrochimica Acta, 2020, 343: 136094
13	吴磊, 吕桃林, 陈启忠, 等. 电化学阻抗谱测量与应用研究综述[J]. 电源技术, 2021, 45(9): 1227-1230.
	WU L, LÜ T L, CHEN Q Z, et al. Review of measurement and application of electrochemical impedance spectroscopy[J]. 2021, 45(9): 1227-1230.
14	范文杰, 徐广昊, 于泊宁, 等. 基于电化学阻抗谱的锂离子电池内部温度在线估计方法研究[J]. 中国电机工程学报, 2021, 41(9): 3283-3293.
	FAN W J, XU G H, YU B N, et al. On-line estimation method for internal temperature of lithium-ion battery based on electrochemical impedance spectroscopy[J]. Proceedings of the CSEE, 2021, 41(9): 3283-3293.
15	马克·欧瑞姆, 伯纳德·特瑞博勒特. 电化学阻抗谱[M]. 雍兴跃, 张学元, 译. 北京: 化学工业出版社, 2014.ORAZEM M E, TRIBOLLET B. Electrochemical impedance spectroscopy[M]. YONG X Y, ZHANG X Y, Trans. Beijing: Chemical Industry Press, 2014.

频率	实部	虚部	模值
300 Hz	Z′₃₀₀	Z″₃₀₀	\|Z\|₃₀₀
60 Hz	Z′₆₀	Z″₆₀	\|Z\|₆₀
1 Hz	Z′₁	Z″₁	\|Z\|₁

[1]	Chenkun LI, Shuai WANG, Jun HUANG. Method for solving physical model of electrochemical impedance spectroscopy [J]. Energy Storage Science and Technology, 2022, 11(3): 912-920.
[2]	Shuai WANG, Hongyan MA, Jiaming DOU, Yingda ZHANG, Shengyan LI, Lujin HU. Estimation of lithium-ion battery state of charge based on UGOA-BP [J]. Energy Storage Science and Technology, 2022, 11(1): 258-264.
[3]	Yuanjin ZHANG, Huawei WU, Congjin YE. Estimation of the SOC of a battery based on the AUKF-BP algorithm [J]. Energy Storage Science and Technology, 2021, 10(1): 237-241.
[4]	Taihua WANG, Shujie ZHANG, Jin'gan CHEN. Low temperature charging performance optimization of lithium battery based on BP-PSO Algorithm [J]. Energy Storage Science and Technology, 2020, 9(6): 1940-1947.
[5]	ZHANG Xinfeng, YAO Mengmeng, WANG Zhongyi, RAO Yongxiang. Lithium-ion battery capacity decline prediction based on ant colony optimization BP neural network algorithm [J]. Energy Storage Science and Technology, 2020, 9(1): 138-144.
[6]	WU Huawei, ZHANG Yuanjin, YE Congjin. Estimation of power battery SOC based on firefly BP neural network [J]. Energy Storage Science and Technology, 2019, 8(3): 575-579.
[7]	LING Shigang, XU Jieru, LI Hong. Experimental measurement and analysis methods of electrochemical impedance spectroscopy for lithium batteries [J]. Energy Storage Science and Technology, 2018, 7(4): 732-749.
[8]	LIU Jian. Second use potential of retired EV batteries in power system and associated cost analysis#br# [J]. Energy Storage Science and Technology, 2017, 6(2): 243-249.
[9]	MA Hongyun, FAN Yongsheng, HONG Weichen, WANG Baoguo. Principle and application of electrochemical impedance spectroscopy method [J]. Energy Storage Science and Technology, 2014, 3(5): 544-549.
[10]	WANG Yongchen, NI Jiangfeng, WANG Haibo, GAO Lijun, HU Daozhong, WANG Zidong. Sorting methods of lithium ion batteries consistency [J]. Energy Storage Science and Technology, 2013, 2(5): 522-527.